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Histopathology images contain essential information for medical diagnosis andprognosis of cancerous disease. Segmentation of glands in histopathology imagesis a primary step for analysis and diagnosis of an unhealthy patient. Due tothe widespread application and the great success of deep neural networks inintelligent medical diagnosis and histopathology, we propose a modified versionof LinkNet for gland segmentation and recognition of malignant cases. We showthat using specific handcrafted features such as invariant local binary patterndrastically improves the system performance. The experimental resultsdemonstrate the competency of the proposed system against state-of-the-artmethods. We achieved the best results in testing on section B images of theWarwick-QU dataset and obtained comparable results on section A images.

Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features